As the functionality of electronic devices continues to increase, most such devices require alphanumeric input and extended controls. Several solutions for data entry for such devices exist like compact keyboards, handwriting recognition, and voice recognition.
Among the possible solutions, handwriting recognition is one of the more popular solutions because it is intuitive, fast, and requires a small footprint on a device. However, handwriting recognition is typically restrictive, requiring sophisticated processing and hardware as well as being less accurate and less flexible than keyboard input.
A primary problem of handwriting recognition is that processing algorithms are quite sophisticated and require considerable processing power. Moreover, even the best algorithms have less than a 100% recognition ratio. Another problem is that handwriting recognition systems are difficult to customize to individual writing styles. All systems are language (alphabet) dependent and require separate, additional modules for every new language. In addition, handwriting recognition requires a high-resolution, sensitive display for symbol input.
Different methods have been proposed to solve the aforementioned problems. Many approaches, such as Unistroke™ from the Xerox Corporation and Graffiti™ from Palm, Inc., require inputting only special, simplified strokes. In these approaches, symbols are typically represented by only one stroke. Using only one stroke simplifies the processing algorithms, but necessitates memorization of the default set of strokes, which precludes easy implementation of these methods by new users.
Due to the complexity of symbol description for traditional handwriting recognition systems and the nature of recognition algorithms, no existing system has 100% recognition ratio, and most typical handwriting recognition systems have much lower recognition ratios that makes these systems unappealing to users.
Some systems provide limited customization of symbols and functions, but that typically increases the complexity of both the system and the hardware requirements.
Some approaches are based on recognition of symbol shapes at a low-resolution rectangular sensitive matrix. Additional restrictions are added on symbol shapes as well as requiring user-memorization of symbol shapes. Due to input limitations, these solutions have simpler processing algorithms and a higher recognition ratio but are usually time-dependent upon the input by the user. As such, these systems typically are not considered to be highly user-friendly in a manner that would appeal to typical users.
Other approaches are based on processing sequences of elements of low-resolution rectangular sensitive matrix. These approaches do not provide natural multi-stroke recognition and customization. These approaches require adjacency of sensitive elements, but do not work correctly with wide input objects like fingers which could activate several elements of the matrix simultaneously.
Further, typical methods for handwriting recognition require the use of a stylus or pen as an input device and a high resolution input device such as a pressure sensitive or resistive touchpad. By using a stylus on a high resolution input device, a processing algorithm may be defined by curve approximations for the symbols represented by single strokes. The need for a stylus is a problem for users of devices such as mobile phones which are typically not used with a stylus and may typically be used with one hand for single-hand entry and operation. For example, a user typically can hold a mobile phone in one hand and operate the device or enter information using the same hand, typically with the thumb of the hand holding the device.
Accordingly, improved data entry systems and associated methods are desired to provide data entry for small electronic devices such mobile phones.